Oct 14, 2025
Problem Statement
A travel recommendation platform aimed to help hotels and OTAs drive guest satisfaction by extracting intelligent insights from multilingual online reviews across multiple sources.
NLP complexity across multiple languages and dialects.
Required hospitality-specific taxonomy with high precision
Difficulty in training models continuously with new review data
Weighted ranking of reviews from different platforms
Solutions
Built and managed a Machine Learning and Lexical Analytics platform using Stanford NLP and proprietary algorithms
Developed multi-level hospitality-specific taxonomy with region-based overrides
Enabled continuous training and deployment using a scalable ML-Ops infrastructure
Integrated NLP and Deep Learning models to extract contextual, sentiment-driven insights
Provided custom dashboards and visualizations to help businesses track service improvements
Business Outcomes:
Technological Framework
Description
AI/ML Frameworks:
Stanford NLP
PyTorch
Keras
TensorFlow
SciKit Learn
Custom Algorithms
Data & Visualization:
OpenCV
Custom Charts
Cassandra
AWS Native Services
DevOps & Infrastructure:
Docker
Python